![]() Observations from the iNaturalist Challenge at FGVC 2017 with links to 675,000 licensed images of 5,089 species have been widely used in computer vision training ( Cui et al. ![]() For instance, iNaturalist includes dozens of metadata fields for every observation and was employed as a case study in the theory of long-tailed datasets ( Cui et al. Many research papers focus on the employment of iNaturalist data as a primary source ( Heberling and Isaac 2018, Seregin et al. The GBIF data usage counter shows that iNaturalist GBIF-mediated data gained 781 citations (as of ) making it one of the most commonly-used datasets amongst the GBIF ( Ueda 2020). Nonetheless, the GBIF export tools provide excellent data usability and the resulting exports come with a DOI which one can use for citation in publications. It is partially represented in the GBIF, with the exclusion of observations which remain unidentified or have unconfirmed or missing licence information. With 50M observations accompanied by photo or audio evidence, the global iNaturalist dataset is one of the largest online collections of biodiversity data. The advent of large, technology-based resources allows ecologists and biologists to work at spatio-temporal scales previously unimaginable ( White et al. There are three key themes that iNaturalist embraces: social interaction shareability of data, tools and code and scalability of the platform and community ( Seltzer 2019). Hundreds of publications have already taken into account iNaturalist data for use in research, conservation and policy (e.g. Since 2008, iNaturalist ( ) has been crowdsourcing biodiversity observations made by citizen scientists, as well as their taxonomic identifications.
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